Collaborative Research: RI: Medium: Thermal Computational Imaging

合作研究：RI：媒介：热计算成像

• 批准号: 2107313
• 负责人: Ashok Veeraraghavan
• 金额: $ 50万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107313&HistoricalAwards=false
• 关键词: Collaborative; Research; RI; Medium; Thermal

Image sensors for thermal wavebands of light are usually noisy and subject to nuisance variations. Yet, it is indisputable that thermal imaging has the potential to spur numerous scientific and engineering applications that can fundamentally transform our society. For example, autonomous vehicles equipped with thermal cameras can navigate in the dark and through fog and rain. The widespread availability of thermal imaging technology could usher in methods for non-intrusive vital sign monitoring in public spaces, thereby providing a new tool in our public health arsenal, as well as enable environmental and ecosystem monitoring at both local and global scales. This project seeks to enable such applications with inexpensive but noisy thermal sensors. The research advances made in this progress will be integrated with an educational and outreach program that includes creating new undergraduate and graduate courses, engaging undergraduates in research, and engaging with K-12 communities.The focus of this research is to advance thermal scene understanding by developing foundational tools for rendering, modeling and imaging at thermal wavebands. The project will develop a physically accurate rendering pipeline for thermal wavebands, that incorporates wavelength-dependent thermal emissivity of complex surfaces, thermal propagation in atmosphere, and accurate sensor modeling and noise characterization. This modeling will be used to design and develop novel computational imaging systems that allow capture of multi-dimensional thermal signals, such as multi-spectral and polarization measurements. The research will also develop a differentiable renderer and exploit it within an end-to-end learning framework for inverse graphics. This will allow for significant improvements in problems such as denoising, super-resolution, stereo, object detection and others. The advances in research will be used to explore and advance autonomous navigation, remote health monitoring and remote environmental monitoring.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

用于光的热波段的图像传感器通常是有噪声的并且经受干扰变化。然而，无可争议的是，热成像有可能刺激许多科学和工程应用，从根本上改变我们的社会。例如，配备热成像摄像头的自动驾驶汽车可以在黑暗中导航，并通过雾和雨。热成像技术的广泛应用可以在公共场所引入非侵入性生命体征监测方法，从而为我们的公共卫生武器库提供新的工具，并在地方和全球范围内实现环境和生态系统监测。该项目旨在通过廉价但有噪声的热传感器实现此类应用。在这一进展中取得的研究进展将与教育和推广计划相结合，包括创建新的本科生和研究生课程，让本科生参与研究，并与K-12社区合作。这项研究的重点是通过开发热波段渲染，建模和成像的基础工具来推进热场景理解。该项目将开发一个针对热波段的物理精确渲染管道，其中包含复杂表面的与波长相关的热发射率、大气中的热传播以及精确的传感器建模和噪声表征。这种建模将用于设计和开发新的计算成像系统，允许捕获多维热信号，如多光谱和偏振测量。该研究还将开发一个可区分的渲染器，并在反向图形的端到端学习框架中利用它。这将使去噪、超分辨率、立体声、物体检测等问题得到显著改善。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Thermal Image Processing via Physics-Inspired Deep Networks

• DOI: 10.1109/iccvw54120.2021.00451
• 发表时间: 2021-08
• 期刊: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
• 作者: Vishwanath Saragadam;Akshat Dave;A. Veeraraghavan;Richard Baraniuk

WIRE: Wavelet Implicit Neural Representations

• DOI: 10.1109/cvpr52729.2023.01775
• 发表时间: 2023-01
• 期刊: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Vishwanath Saragadam;Daniel LeJeune;Jasper Tan;Guha Balakrishnan;A. Veeraraghavan;Richard Baraniuk

Thermal Spread Functions (TSF): Physics-Guided Material Classification

• DOI: 10.1109/cvpr52729.2023.00164
• 发表时间: 2023-04
• 期刊: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Aniket Dashpute;Vishwanath Saragadam;Emma Alexander;F. Willomitzer;A. Katsaggelos;A. Veeraraghavan;O. Cossairt